Princeton Visual AI Lab

  1. Art and the Science of Generative AI

    Ziv Epstein, Aaron Hertzmann and the Investigators of Human Creativity (Memo Akten, Hany Farid, Jessica Fjeld, Morgan R. Frank, Matthew Groh, Laura Herman, Neil Leach, Robert Mahari, Alex Pentland, Olga Russakovsky, Hope Schroeder, Amy Smith).

    Science Perspectives, 2023.

    [paper] [extended white paper] [bibtex]
  2. Enabling Detailed Action Recognition Evaluation Through Video Dataset Augmentation

    Jihoon Chung, Yu Wu and Olga Russakovsky.

    Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2022.

    [paper] [code] [bibtex]
  3. Multi-Query Video Retrieval

    Zeyu Wang, Yu Wu, Karthik Narasimhan and Olga Russakovsky.

    European Conference on Computer Vision (ECCV), 2022.

    [paper] [code] [bibtex]
  4. A Study of Face Obfuscation in ImageNet

    Kaiyu Yang, Jacqueline Yau, Li Fei-Fei, Jia Deng and Olga Russakovsky.

    International Conference on Machine Learning (ICML), 2022.

    [paper] [project] [code] [bibtex]
  5. Directional Bias Amplification

    Angelina Wang and Olga Russakovsky.

    International Conference on Machine Learning (ICML), 2021.

    [paper] [code] [bibtex]
  6. CornerNet-Lite: Efficient Keypoint Based Object Detection

    Hei Law, Yun Teng, Olga Russakovsky and Jia Deng.

    British Machine Vision Conference (BMVC), 2020.

    [paper] [code] [bibtex]
  7. Towards Unique and Informative Captioning of Images

    Zeyu Wang, Berthy Feng, Karthik Narasimhan and Olga Russakovsky.

    European Conference on Computer Vision (ECCV), 2020.

    [paper] [code] [1-min video] [10-min video] [bibtex]
  8. Human Uncertainty Makes Classification More Robust

    Joshua C. Peterson*, Ruairidh M. Battleday*, Thomas L. Griffiths and Olga Russakovsky. (* = equal contribution)

    International Conference on Computer Vision (ICCV), 2019.

    [paper] [bibtex]
  9. Predictive-Corrective Networks for Action Detection

    Achal Dave, Olga Russakovsky and Deva Ramanan.

    Computer Vision and Pattern Recognition (CVPR), 2017.

    [paper] [project] [bibtex]
  10. Learning to Learn from Noisy Web Videos

    Serena Yeung, Vignesh Ramanathan, Olga Russakovsky, Liyue Shen, Greg Mori and Li Fei-Fei.

    Computer Vision and Pattern Recognition (CVPR), 2017.

    [paper] [poster] [bibtex]
  11. Crowdsourcing in Computer Vision
    Adriana Kovashka, Olga Russakovsky, Li Fei-Fei and Kristen Grauman.

    Foundation and Trends in Computer Vision and Graphics, 2016.

    [paper] [bibtex]
  12. Towards More Gender Diversity in CS through an Artificial Intelligence Summer Program for High School Girls

    Marie E. Vachovsky, Grace Wu, Sorathan Chaturapruek, Olga Russakovsky, Rick Sommer, Li Fei-Fei.

    Special Interest Group on Computer Science Education (SIGCSE), 2016.

    [paper] [SAILORS camp homepage] [Wired article] [bibtex]
  13. Scaling up Object Detection

    Olga Russakovsky.

    PhD Thesis, Stanford University, 2015.

    [paper] [poster] [bibtex]
  14. Scalable Multi-Label Annotation

    Jia Deng, Olga Russakovsky, Jonathan Krause, Michael Bernstein, Alexander Berg and Li Fei-Fei.

    ACM Conference on Human Factors in Computing Systems (CHI), 2014.

    [paper] [bibtex] [slides]
  15. Attribute learning in large-scale data

    Olga Russakovsky and Li Fei-Fei.

    Parts and Attributes Workshop at European Conference on Computer Vision (ECCVW), 2010.

    [pdf] [bibtex] [slides odpslides pdf] [data]
  16. A Steiner tree approach to efficient object detection.

    Olga Russakovsky and Andrew Y. Ng.

    Computer Vision and Pattern Recognition (CVPR), 2010.

    [paper] [bibtex] [poster] [data]
  17. Autonomous operation of novel elevators for robot navigation

    Ellen Klingbeil, Blake Carpenter, Olga Russakovsky, Andrew Y. Ng.

    International Conference on Robotics and Automation (ICRA), 2010.

    [paper] [bibtex]
  18. Training Conditional Random Fields for maximum labelwise accuracy

    Samuel S. Gross, Olga Russakovsky, Chuong B. Do and Serafim Batzoglou.

    Advances in Neural Information Processing Systems (NeurIPS), 2007.

    [paper] [bibtex]